issue_comments

I was not aware of dask's atop function before reading this PR (it looks pretty cool), so I defer to @nbren12 there.

@nbren12 for similar use cases I've had success writing a single function that does the ghosting, applies a function with map_blocks, and trims the edges. Then I apply that single function on a DataArray with apply_ufunc (so a single call to apply_ufunc rather than three). As an example, a simple centered difference on an array with periodic boundaries might be accomplished with: ```python def centered_diff_numpy(arr, axis=-1, spacing=1.): return (np.roll(arr, -1, axis=axis) - np.roll(arr, 1, axis=axis)) / (2. * spacing)

def centered_diff(da, dim, spacing=1.): def apply_centered_diff(arr, spacing=1.): if isinstance(arr, np.ndarray): return centered_diff_numpy(arr, spacing=spacing) else: axis = len(arr.shape) - 1 g = darray.ghost.ghost(arr, depth={axis: 1}, boundary={axis: 'periodic'}) result = darray.map_blocks(centered_diff_numpy, g, spacing=spacing) return darray.ghost.trim_internal(result, {axis: 1})

return computation.apply_ufunc(
    apply_centered_diff, da, input_core_dims=[[dim]],
    output_core_dims=[[dim]], dask_array='allowed', kwargs={'spacing': spacing})

Depending on your use case, you might also consider `dask.ghost.map_overlap` to do all of those three steps in one line, i.e. replace `apply_centered_diff` with the following:python def apply_centered_diff(arr, spacing=1.): if isinstance(arr, np.ndarray): return centered_diff_numpy(arr, spacing=spacing) else: axis = len(arr.shape) - 1 return darray.ghost.map_overlap( arr, centered_diff_numpy, depth={axis: 1}, boundary={axis: 'periodic'}, spacing=spacing) ``` (Not sure if this is what @shoyer had in mind, but just offering an example)